Monte Carlo Evidence On Cointegration And Causation
AbstractThe small sample performance of Granger causality tests under different model dimensions, degree of cointegration, direction of causality, and system stability are presented. Two tests based on maximum likelihood estimation of error-correction models (LR and WALD) are compared to a Wald test based on multivariate least squares estimation of a modified VAR (MWALD). In large samples all test statistics perform well in terms of size and power. For smaller samples, the LR and WALD tests perform better than the MWALD test. Overall, the LR test outperforms the other two in terms of size and power in small samples.
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Bibliographic InfoPaper provided by Louisiana State University, Department of Agricultural Economics and Agribusiness in its series Staff Papers with number 31690.
Date of creation: 1996
Date of revision:
Causality tests; Cointegration; Likelihood ratio; Wald statistic; Monte Carlo Experiments; Research Methods/ Statistical Methods;
Other versions of this item:
- Zapata, Hector O & Rambaldi, Alicia N, 1997. "Monte Carlo Evidence on Cointegration and Causation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(2), pages 285-98, May.
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